def _config_raw_from_input(self, config=None, name=None, n_epochs=None, seed=None, append_rnd_to_name=False): """Construct _config_raw from input.""" _config_raw = None if isinstance(config, str): _config_raw = Config(file_=config) elif isinstance(config, (Config, dict)): _config_raw = Config(config=config) else: _config_raw = Config() if n_epochs is None and _config_raw.get("n_epochs") is not None: n_epochs = _config_raw["n_epochs"] elif n_epochs is None and _config_raw.get("n_epochs") is None: n_epochs = 0 _config_raw["n_epochs"] = n_epochs if seed is None and _config_raw.get('seed') is not None: seed = _config_raw['seed'] elif seed is None and _config_raw.get('seed') is None: random_data = os.urandom(4) seed = int.from_bytes(random_data, byteorder="big") _config_raw['seed'] = seed if name is None and _config_raw.get("name") is not None: name = _config_raw["name"] elif name is None and _config_raw.get("name") is None: name = "experiment" if append_rnd_to_name: rnd_str = ''.join( random.choice(string.ascii_letters + string.digits) for _ in range(5)) name += "_" + rnd_str _config_raw["name"] = name return _config_raw
class PytorchExperiment(Experiment): """ A PytorchExperiment extends the basic functionality of the :class:`.Experiment` class with convenience features for PyTorch (and general logging) such as creating a folder structure, saving, plotting results and checkpointing your experiment. The basic life cycle of a PytorchExperiment is the same as :class:`.Experiment`:: setup() prepare() for epoch in n_epochs: train() validate() end() where the distinction between the first two is that between them PytorchExperiment will automatically restore checkpoints and save the :attr:`_config_raw` in :meth:`._setup_internal`. Please see below for more information on this. To get your own experiment simply inherit from the PytorchExperiment and overwrite the :meth:`.setup`, :meth:`.prepare`, :meth:`.train`, :meth:`.validate` method (or you can use the `very` experimental decorator :func:`.experimentify` to convert your class into a experiment). Then you can run your own experiment by calling the :meth:`.run` method. Internally PytorchExperiment will provide a number of member variables which you can access. - n_epochs Number of epochs. - exp_name Name of your experiment. - config The (initialized) :class:`.Config` of your experiment. You can access the uninitialized one via :attr:`_config_raw`. - result A dict in which you can store your result values. If a :class:`.PytorchExperimentLogger` is used, results will be a :class:`.ResultLogDict` that directly automatically writes to a file and also stores the N last entries for each key for quick access (e.g. to quickly get the running mean). - elog (if base_dir is given) A :class:`.PytorchExperimentLogger` instance which can log your results to a given folder. Will automatically be created if a base_dir is available. - loggers Contains all loggers you provide, including the experiment logger, accessible by the names you provide. - clog A :class:`.CombinedLogger` instance which logs to all loggers with different frequencies (specified with the last entry in the tuple you provide for each logger where 1 means every time and N means every Nth time, e.g. if you only want to send stuff to Visdom every 10th time). The most important attribute is certainly :attr:`.config`, which is the initialized :class:`.Config` for the experiment. To understand how it needs to be structured to allow for automatic instantiation of types, please refer to its documentation. If you decide not to use this functionality, :attr:`config` and :attr:`_config_raw` are identical. **Beware however that by default the Pytorchexperiment only saves the raw config** after :meth:`.setup`. If you modify :attr:`config` during setup, make sure to implement :meth:`._setup_internal` yourself should you want the modified config to be saved:: def _setup_internal(self): super(YourExperiment, self)._setup_internal() # calls .prepare_resume() self.elog.save_config(self.config, "config") Args: config (dict or Config): Configures your experiment. If :attr:`name`, :attr:`n_epochs`, :attr:`seed`, :attr:`base_dir` are given in the config, it will automatically overwrite the other args/kwargs with the values from the config. In addition (defined by :attr:`parse_config_sys_argv`) the config automatically parses the argv arguments and updates its values if a key matches a console argument. name (str): The name of the PytorchExperiment. n_epochs (int): The number of epochs (number of times the training cycle will be executed). seed (int): A random seed (which will set the random, numpy and torch seed). base_dir (str): A base directory in which the experiment result folder will be created. A :class:`.PytorchExperimentLogger` instance will be created if this is given. globs: The :func:`globals` of the script which is run. This is necessary to get and save the executed files in the experiment folder. resume (str or PytorchExperiment): Another PytorchExperiment or path to the result dir from another PytorchExperiment from which it will load the PyTorch modules and other member variables and resume the experiment. ignore_resume_config (bool): If :obj:`True` it will not resume with the config from the resume experiment but take the current/own config. resume_save_types (list or tuple): A list which can define which values to restore when resuming. Choices are: - "model" <-- Pytorch models - "optimizer" <-- Optimizers - "simple" <-- Simple python variables (basic types and lists/tuples - "th_vars" <-- torch tensors/variables - "results" <-- The result dict resume_reset_epochs (bool): Set epoch to zero if you resume an existing experiment. parse_sys_argv (bool): Parsing the console arguments (argv) to get a :attr:`config path` and/or :attr:`resume_path`. parse_config_sys_argv (bool): Parse argv to update the config (if the keys match). checkpoint_to_cpu (bool): When checkpointing, transfer all tensors to the CPU beforehand. save_checkpoint_every_epoch (int): Determines after how many epochs a checkpoint is stored. explogger_kwargs (dict): Keyword arguments for :attr:`elog` instantiation. explogger_freq (int): The frequency x (meaning one in x) with which the :attr:`clog` will call the :attr:`elog`. loggers (dict): Specify additional loggers. Entries should have one of these formats:: "name": "identifier" (will default to a frequency of 10) "name": ("identifier"(, kwargs, frequency)) (last two are optional) "identifier" is one of "telegram", "tensorboard", "visdom", "slack". append_rnd_to_name (bool): If :obj:`True`, will append a random six digit string to the experiment name. """ def __init__(self, config=None, name=None, n_epochs=None, seed=None, base_dir=None, globs=None, resume=None, ignore_resume_config=False, resume_save_types=("model", "optimizer", "simple", "th_vars", "results"), resume_reset_epochs=True, parse_sys_argv=False, checkpoint_to_cpu=True, save_checkpoint_every_epoch=1, explogger_kwargs=None, explogger_freq=1, loggers=None, append_rnd_to_name=False, default_save_types=("model", "optimizer", "simple", "th_vars", "results")): # super(PytorchExperiment, self).__init__() Experiment.__init__(self) # check for command line inputs for config_path and resume_path, # will be prioritized over config and resume! config_path_from_argv = None if parse_sys_argv: config_path_from_argv, resume_path_from_argv = get_vars_from_sys_argv( ) if resume_path_from_argv: resume = resume_path_from_argv # construct _config_raw if config_path_from_argv is None: self._config_raw = self._config_raw_from_input( config, name, n_epochs, seed, append_rnd_to_name) else: self._config_raw = Config(file_=config_path_from_argv) update_from_sys_argv(self._config_raw) # set a few experiment attributes self.n_epochs = self._config_raw["n_epochs"] self._seed = self._config_raw['seed'] set_seed(self._seed) self.exp_name = self._config_raw["name"] self._checkpoint_to_cpu = checkpoint_to_cpu self._save_checkpoint_every_epoch = save_checkpoint_every_epoch self._default_save_types = ("model", "optimizer", "simple", "th_vars", "results") self.results = dict() # get base_dir from _config_raw or store there if base_dir is not None: self._config_raw["base_dir"] = base_dir base_dir = self._config_raw["base_dir"] # Construct experiment logger (automatically activated if base_dir is there) self.loggers = {} logger_list = [] if base_dir is not None: if explogger_kwargs is None: explogger_kwargs = {} self.elog = PytorchExperimentLogger(base_dir=base_dir, exp_name=self.exp_name, **explogger_kwargs) if explogger_freq is not None and explogger_freq > 0: logger_list.append((self.elog, explogger_freq)) self.results = ResultLogDict("results-log.json", base_dir=self.elog.result_dir) else: self.elog = None # Construct other loggers if loggers is not None: for logger_name, logger_cfg in loggers.items(): _logger, log_freq = self._make_logger(logger_name, logger_cfg) self.loggers[logger_name] = _logger if log_freq is not None and log_freq > 0: logger_list.append((_logger, log_freq)) self.clog = CombinedLogger(*logger_list) # Set resume attributes and update _config_raw, # actual resuming is done automatically after setup in _setup_internal self._resume_path = None self._resume_save_types = resume_save_types self._ignore_resume_config = ignore_resume_config self._resume_reset_epochs = resume_reset_epochs if resume is not None: if isinstance(resume, str): if resume == "last": if base_dir is None: raise ValueError("resume='last' requires base_dir.") self._resume_path = os.path.join( base_dir, sorted(os.listdir(base_dir))[-1]) else: self._resume_path = resume elif isinstance(resume, PytorchExperiment): self._resume_path = resume.elog.base_dir if self._resume_path is not None and not self._ignore_resume_config: self._config_raw.update(Config(file_=os.path.join( self._resume_path, "config", "config.json")), ignore=list( map(lambda x: re.sub("^-+", "", x), sys.argv))) # Save everything we need to reproduce experiment if globs is not None and self.elog is not None: zip_name = os.path.join(self.elog.save_dir, "sources.zip") SourcePacker.zip_sources(globs, zip_name) # Init objects in config self.config = Config.init_objects(self._config_raw) atexit.register(self.at_exit_func) def _config_raw_from_input(self, config=None, name=None, n_epochs=None, seed=None, append_rnd_to_name=False): """Construct _config_raw from input.""" _config_raw = None if isinstance(config, str): _config_raw = Config(file_=config) elif isinstance(config, (Config, dict)): _config_raw = Config(config=config) else: _config_raw = Config() if n_epochs is None and _config_raw.get("n_epochs") is not None: n_epochs = _config_raw["n_epochs"] elif n_epochs is None and _config_raw.get("n_epochs") is None: n_epochs = 0 _config_raw["n_epochs"] = n_epochs if seed is None and _config_raw.get('seed') is not None: seed = _config_raw['seed'] elif seed is None and _config_raw.get('seed') is None: random_data = os.urandom(4) seed = int.from_bytes(random_data, byteorder="big") _config_raw['seed'] = seed if name is None and _config_raw.get("name") is not None: name = _config_raw["name"] elif name is None and _config_raw.get("name") is None: name = "experiment" if append_rnd_to_name: rnd_str = ''.join( random.choice(string.ascii_letters + string.digits) for _ in range(5)) name += "_" + rnd_str _config_raw["name"] = name return _config_raw def _make_logger(self, logger_name, logger_cfg): if isinstance(logger_cfg, (list, tuple)): log_type = logger_cfg[0] log_params = logger_cfg[1] if len(logger_cfg) > 1 else {} log_freq = logger_cfg[2] if len(logger_cfg) > 2 else 10 else: assert isinstance(logger_cfg, str), "The specified logger has to either be a string or a list with " \ "name, parameters, clog_frequency" log_type = logger_cfg log_params = {} log_freq = 10 if "exp_name" not in log_params: log_params["exp_name"] = self.exp_name if log_type == "tensorboard": if "target_dir" not in log_params or log_params[ "target_dir"] is None: if self.elog is not None: log_params["target_dir"] = os.path.join( self.elog.save_dir, "tensorboard") else: raise AttributeError( "TensorboardLogger requires a target_dir or an ExperimentLogger instance." ) elif self.elog is not None: log_params["target_dir"] = os.path.join( log_params["target_dir"], self.elog.folder_name) log_type = logger_lookup_dict[log_type] _logger = log_type(**log_params) return _logger, log_freq @property def vlog(self): if "visdom" in self.loggers: return self.loggers["visdom"] elif "v" in self.loggers: return self.loggers["v"] else: return None @property def tlog(self): if "telegram" in self.loggers: return self.loggers["telegram"] elif "t" in self.loggers: return self.loggers["t"] else: return None @property def txlog(self): if "tensorboard" in self.loggers: return self.loggers["tensorboard"] if "tensorboardx" in self.loggers: return self.loggers["tensorboardx"] elif "tx" in self.loggers: return self.loggers["tx"] else: return None @property def slog(self): if "slack" in self.loggers: return self.loggers["slack"] elif "s" in self.loggers: return self.loggers["s"] else: return None def process_err(self, e): if self.elog is not None: self.elog.text_logger.log_to( "\n".join(traceback.format_tb(e.__traceback__)), "err") self.elog.text_logger.log_to(repr(e), "err") raise e def update_attributes(self, var_dict, ignore=()): """ Updates the member attributes with the attributes given in the var_dict Args: var_dict (dict): dict in which the update values stored. If a key matches a member attribute name the member attribute will be updated ignore (list or tuple): iterable of keys to ignore """ for key, val in var_dict.items(): if key == "results": self.results.load(val) continue if key in ignore: continue if hasattr(self, key): setattr(self, key, val) def get_pytorch_modules(self, from_config=True): """ Returns all torch.nn.Modules stored in the experiment in a dict (even child dicts are stored). Args: from_config (bool): Also get modules that are stored in the :attr:`.config` attribute. Returns: dict: Dictionary of PyTorch modules """ def parse_torchmodules_recursive(input, output): if isinstance(input, dict): for key, value in input.items(): if isinstance(value, dict): parse_torchmodules_recursive(value, output) elif isinstance(value, torch.nn.Module): output[key] = value pyth_modules = dict() parse_torchmodules_recursive(self.__dict__, pyth_modules) # for key, val in self.__dict__.items(): # if isinstance(val, torch.nn.Module): # pyth_modules[key] = val if from_config: for key, val in self.config.items(): if isinstance(val, torch.nn.Module): if type(key) == str: key = "config." + key pyth_modules[key] = val return pyth_modules def get_pytorch_optimizers(self, from_config=True): """ Returns all torch.optim.Optimizers stored in the experiment in a dict. Args: from_config (bool): Also get optimizers that are stored in the :attr:`.config` attribute. Returns: dict: Dictionary of PyTorch optimizers """ pyth_optimizers = dict() for key, val in self.__dict__.items(): if isinstance(val, torch.optim.Optimizer): pyth_optimizers[key] = val if from_config: for key, val in self.config.items(): if isinstance(val, torch.optim.Optimizer): if type(key) == str: key = "config." + key pyth_optimizers[key] = val return pyth_optimizers def get_simple_variables(self, ignore=()): """ Returns all standard variables in the experiment in a dict. Specifically, this looks for types :class:`int`, :class:`float`, :class:`bytes`, :class:`bool`, :class:`str`, :class:`set`, :class:`list`, :class:`tuple`. Args: ignore (list or tuple): Iterable of names which will be ignored Returns: dict: Dictionary of variables """ simple_vars = dict() for key, val in self.__dict__.items(): if key in ignore: continue if isinstance(val, (int, float, bytes, bool, str, set, list, tuple)): simple_vars[key] = val return simple_vars def get_pytorch_tensors(self, ignore=()): """ Returns all torch.tensors in the experiment in a dict. Args: ignore (list or tuple): Iterable of names which will be ignored Returns: dict: Dictionary of PyTorch tensor """ pytorch_vars = dict() for key, val in self.__dict__.items(): if key in ignore: continue if torch.is_tensor(val): pytorch_vars[key] = val return pytorch_vars def get_pytorch_variables(self, ignore=()): """Same as :meth:`.get_pytorch_tensors`.""" return self.get_pytorch_tensors(ignore) def save_results(self, name="results.json"): """ Saves the result dict as a json file in the result dir of the experiment logger. Args: name (str): The name of the json file in which the results are written. """ if self.elog is None: return with open(os.path.join(self.elog.result_dir, name), "w") as file_: json.dump(self.results, file_, indent=4) def save_pytorch_models(self): """Saves all torch.nn.Modules as model files in the experiment checkpoint folder.""" if self.elog is None: return pyth_modules = self.get_pytorch_modules() for key, val in pyth_modules.items(): self.elog.save_model(val, key) def load_pytorch_models(self): """Loads all model files from the experiment checkpoint folder.""" if self.elog is None: return pyth_modules = self.get_pytorch_modules() for key, val in pyth_modules.items(): self.elog.load_model(val, key) def log_simple_vars(self): """ Logs all simple python member variables as a json file in the experiment log folder. The file will be names 'simple_vars.json'. """ if self.elog is None: return simple_vars = self.get_simple_variables() with open(os.path.join(self.elog.log_dir, "simple_vars.json"), "w") as file_: json.dump(simple_vars, file_) def load_simple_vars(self): """ Restores all simple python member variables from the 'simple_vars.json' file in the log folder. """ if self.elog is None: return simple_vars = {} with open(os.path.join(self.elog.log_dir, "simple_vars.json"), "r") as file_: simple_vars = json.load(file_) self.update_attributes(simple_vars) def save_checkpoint(self, name="checkpoint", save_types=("model", "optimizer", "simple", "th_vars", "results"), n_iter=None, iter_format="{:05d}", prefix=False): """ Saves a current model checkpoint from the experiment. Args: name (str): The name of the checkpoint file save_types (list or tuple): What kind of member variables should be stored? Choices are: "model" <-- Pytorch models, "optimizer" <-- Optimizers, "simple" <-- Simple python variables (basic types and lists/tuples), "th_vars" <-- torch tensors, "results" <-- The result dict n_iter (int): Number of iterations. Together with the name, defined by the iter_format, a file name will be created. iter_format (str): Defines how the name and the n_iter will be combined. prefix (bool): If True, the formatted n_iter will be prepended, otherwise appended. """ if self.elog is None: return model_dict = {} optimizer_dict = {} simple_dict = {} th_vars_dict = {} results_dict = {} if "model" in save_types: model_dict = self.get_pytorch_modules() if "optimizer" in save_types: optimizer_dict = self.get_pytorch_optimizers() if "simple" in save_types: simple_dict = self.get_simple_variables() if "th_vars" in save_types: th_vars_dict = self.get_pytorch_variables() if "results" in save_types: results_dict = {"results": self.results} checkpoint_dict = { **model_dict, **optimizer_dict, **simple_dict, **th_vars_dict, **results_dict } self.elog.save_checkpoint(name=name, n_iter=n_iter, iter_format=iter_format, prefix=prefix, move_to_cpu=self._checkpoint_to_cpu, **checkpoint_dict) def load_checkpoint(self, name="checkpoint", save_types=("model", "optimizer", "simple", "th_vars", "results"), n_iter=None, iter_format="{:05d}", prefix=False, path=None): """ Loads a checkpoint and restores the experiment. Make sure you have your torch stuff already on the right devices beforehand, otherwise this could lead to errors e.g. when making a optimizer step (and for some reason the Adam states are not already on the GPU: https://discuss.pytorch.org/t/loading-a-saved-model-for-continue-training/17244/3 ) Args: name (str): The name of the checkpoint file save_types (list or tuple): What kind of member variables should be loaded? Choices are: "model" <-- Pytorch models, "optimizer" <-- Optimizers, "simple" <-- Simple python variables (basic types and lists/tuples), "th_vars" <-- torch tensors, "results" <-- The result dict n_iter (int): Number of iterations. Together with the name, defined by the iter_format, a file name will be created and searched for. iter_format (str): Defines how the name and the n_iter will be combined. prefix (bool): If True, the formatted n_iter will be prepended, otherwise appended. path (str): If no path is given then it will take the current experiment dir and formatted name, otherwise it will simply use the path and the formatted name to define the checkpoint file. """ if self.elog is None: return model_dict = {} optimizer_dict = {} simple_dict = {} th_vars_dict = {} results_dict = {} if "model" in save_types: model_dict = self.get_pytorch_modules() if "optimizer" in save_types: optimizer_dict = self.get_pytorch_optimizers() if "simple" in save_types: simple_dict = self.get_simple_variables() if "th_vars" in save_types: th_vars_dict = self.get_pytorch_variables() if "results" in save_types: results_dict = {"results": self.results} checkpoint_dict = { **model_dict, **optimizer_dict, **simple_dict, **th_vars_dict, **results_dict } if n_iter is not None: name = name_and_iter_to_filename(name, n_iter, ".pth.tar", iter_format=iter_format, prefix=prefix) if path is None: restore_dict = self.elog.load_checkpoint(name=name, **checkpoint_dict) else: checkpoint_path = os.path.join(path, name) if checkpoint_path.endswith(os.sep): checkpoint_path = os.path.dirname(checkpoint_path) restore_dict = self.elog.load_checkpoint_static( checkpoint_file=checkpoint_path, **checkpoint_dict) self.update_attributes(restore_dict) def _end_internal(self): """Ends the experiment and stores the final results/checkpoint""" if isinstance(self.results, ResultLogDict): self.results.close() self.save_results() self.save_end_checkpoint() self._save_exp_config() self.print("Experiment ended. Checkpoints stored =)") def _end_test_internal(self): """Ends the experiment after test and stores the final results and config""" self.save_results() self._save_exp_config() self.print("Testing ended. Results stored =)") def at_exit_func(self): """ Stores the results and checkpoint at the end (if not already stored). This method is also called if an error occurs. """ if self._exp_state not in ("Ended", "Tested"): if isinstance(self.results, ResultLogDict): self.results.print_to_file("]") self.save_checkpoint(name="checkpoint_exit-" + self._exp_state, save_types=self._default_save_types) self.save_results() self._save_exp_config() self.print("Experiment exited. Checkpoints stored =)") time.sleep( 2 ) # allow checkpoint saving to finish. We need a better solution for this :D def _setup_internal(self): self.prepare_resume() if self.elog is not None: self.elog.save_config(self._config_raw, "config") self._save_exp_config() def _start_internal(self): self._save_exp_config() def prepare_resume(self): """Tries to resume the experiment by using the defined resume path or PytorchExperiment.""" checkpoint_file = "" base_dir = "" reset_epochs = self._resume_reset_epochs if self._resume_path is not None: if isinstance(self._resume_path, str): if self._resume_path.endswith(".pth.tar"): checkpoint_file = self._resume_path base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) elif self._resume_path.endswith( "checkpoint") or self._resume_path.endswith( "checkpoint/"): checkpoint_file = get_last_file(self._resume_path) base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) elif "checkpoint" in os.listdir( self._resume_path) and "config" in os.listdir( self._resume_path): checkpoint_file = get_last_file(self._resume_path) base_dir = self._resume_path else: warnings.warn( "You have not selected a valid experiment folder, will search all sub folders", UserWarning) if self.elog is not None: self.elog.text_logger.log_to( "You have not selected a valid experiment folder, will search all " "sub folders", "warnings") checkpoint_file = get_last_file(self._resume_path) base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) if base_dir: if not self._ignore_resume_config: load_config = Config() load_config.load(os.path.join(base_dir, "config/config.json")) self._config_raw = load_config self.config = Config.init_objects(self._config_raw) self.print("Loaded existing config from:", base_dir) if self.n_epochs is None: self.n_epochs = self._config_raw.get("n_epochs") if checkpoint_file: self.load_checkpoint(name="", path=checkpoint_file, save_types=self._resume_save_types) self._resume_path = checkpoint_file shutil.copyfile( checkpoint_file, os.path.join(self.elog.checkpoint_dir, "0_checkpoint.pth.tar")) self.print("Loaded existing checkpoint from:", checkpoint_file) self._resume_reset_epochs = reset_epochs if self._resume_reset_epochs: self._epoch_idx = 0 def _end_epoch_internal(self, epoch): self.save_results() if self._save_checkpoint_every_epoch is not None and self._save_checkpoint_every_epoch > 0 and epoch % \ self._save_checkpoint_every_epoch == 0: self.save_temp_checkpoint() self._save_exp_config() def _save_exp_config(self): if self.elog is not None: cur_time = time.strftime("%y-%m-%d_%H:%M:%S", time.localtime(time.time())) self.elog.save_config( Config( **{ 'name': self.exp_name, 'time': self._time_start, 'state': self._exp_state, 'current_time': cur_time, 'epoch': self._epoch_idx }), "exp") def save_temp_checkpoint(self): """Saves the current checkpoint as checkpoint_current.""" self.save_checkpoint(name="checkpoint_current", save_types=self._default_save_types) def save_end_checkpoint(self): """Saves the current checkpoint as checkpoint_last.""" self.save_checkpoint(name="checkpoint_last", save_types=self._default_save_types) def add_result(self, value, name, counter=None, tag=None, label=None, plot_result=True, plot_running_mean=False): """ Saves a results and add it to the result dict, this is similar to results[key] = val, but in addition also logs the value to the combined logger (it also stores in the results-logs file). **This should be your preferred method to log your numeric values** Args: value: The value of your variable name (str): The name/key of your variable counter (int or float): A counter which can be seen as the x-axis of your value. Normally you would just use the current epoch for this. tag (str): A label/tag which can group similar values and will plot values with the same label in the same plot label: deprecated label plot_result (bool): By default True, will also log all your values to the combined logger (with show_value). """ if label is not None: warnings.warn( "label in add_result is deprecated, please use tag instead") if tag is None: tag = label tag_name = tag if tag_name is None: tag_name = name r_elem = ResultElement(data=value, label=tag_name, epoch=self._epoch_idx, counter=counter) self.results[name] = r_elem if plot_result: if tag is None: legend = False else: legend = True if plot_running_mean: value = np.mean(self.results.running_mean_dict[name]) self.clog.show_value(value=value, name=name, tag=tag_name, counter=counter, show_legend=legend) def get_result(self, name): """ Similar to result[key] this will return the values in the results dictionary with the given name/key. Args: name (str): the name/key for which a value is stored. Returns: The value with the key 'name' in the results dict. """ return self.results.get(name) def add_result_without_epoch(self, val, name): """ A faster method to store your results, has less overhead and does not call the combined logger. Will only store to the results dictionary. Args: val: the value you want to add. name (str): the name/key of your value. """ self.results[name] = val def get_result_without_epoch(self, name): """ Similar to result[key] this will return the values in result with the given name/key. Args: name (str): the name/ key for which a value is stores. Returns: The value with the key 'name' in the results dict. """ return self.results.get(name) def print(self, *args): """ Calls 'print' on the experiment logger or uses builtin 'print' if former is not available. """ if self.elog is None: print(*args) else: self.elog.print(*args)